Before big data and fast data, the challenge of data in motion was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line. In today’s world, consuming applications and routes and rules for moving data constantly change. Big data processing operations are more like a city traffic grid than the linear path taken by traditional data. The emerging world is many-to-many, with streaming or micro-batched data coming from numerous sources and being consumed by numerous applications. Because modern data is so dynamic, dealing with data in motion requires a full lifecycle perspective including day-to-day operations and agility over time. Organizations must tune the performance of their data movement system as both data infrastructure and business requirements for the use of data evolve.